weka控制台指令

java weka.classifiers.trees.J48 -t data/weather.arff

java 类的完整名称 -t表示下一个参数是训练数据集的名称

java weka.classifiers.trees.J48 -h

查看java命令行中各个参数的具体含义

-h or -help
    Output help information.
-synopsis or -info
    Output synopsis for classifier (use in conjunction  with -h)
-t <name of training file>
    Sets training file.
-T <name of test file>
    Sets test file. If missing, a cross-validation will be performed
    on the training data.
-c <class index>
    Sets index of class attribute (default: last).
-x <number of folds>
    Sets number of folds for cross-validation (default: 10).
-no-cv
    Do not perform any cross validation.
-force-batch-training
    Always train classifier in batch mode, never incrementally.
-split-percentage <percentage>
    Sets the percentage for the train/test set split, e.g., 66.
-preserve-order
    Preserves the order in the percentage split.
-s <random number seed>
    Sets random number seed for cross-validation or percentage split
    (default: 1).
-m <name of file with cost matrix>
    Sets file with cost matrix.
-disable <comma-separated list of evaluation metric names>
    Comma separated list of metric names not to print to the output.
    Available metrics:
    Correct,Incorrect,Kappa,Total cost,Average cost,KB relative,KB information,
    Correlation,Complexity 0,Complexity scheme,Complexity improvement,
    MAE,RMSE,RAE,RRSE,Coverage,Region size,TP rate,FP rate,Precision,Recall,
    F-measure,MCC,ROC area,PRC area
-l <name of input file>
    Sets model input file. In case the filename ends with ‘.xml‘,
    a PMML file is loaded or, if that fails, options are loaded
    from the XML file.
-d <name of output file>
    Sets model output file. In case the filename ends with ‘.xml‘,
    only the options are saved to the XML file, not the model.
-v
    Outputs no statistics for training data.
-o
    Outputs statistics only, not the classifier.
-i
    Outputs detailed information-retrieval statistics for each class.
-k
    Outputs information-theoretic statistics.
-classifications "weka.classifiers.evaluation.output.prediction.AbstractOutput + options"
    Uses the specified class for generating the classification output.
    E.g.: weka.classifiers.evaluation.output.prediction.PlainText
-p range
    Outputs predictions for test instances (or the train instances if
    no test instances provided and -no-cv is used), along with the
    attributes in the specified range (and nothing else).
    Use ‘-p 0‘ if no attributes are desired.
    Deprecated: use "-classifications ..." instead.
-distribution
    Outputs the distribution instead of only the prediction
    in conjunction with the ‘-p‘ option (only nominal classes).
    Deprecated: use "-classifications ..." instead.
-r
    Only outputs cumulative margin distribution.
-z <class name>
    Only outputs the source representation of the classifier,
    giving it the supplied name.
-g
    Only outputs the graph representation of the classifier.
-xml filename | xml-string
    Retrieves the options from the XML-data instead of the command line.
-threshold-file <file>
    The file to save the threshold data to.
    The format is determined by the extensions, e.g., ‘.arff‘ for ARFF
    format or ‘.csv‘ for CSV.
-threshold-label <label>
    The class label to determine the threshold data for
    (default is the first label)

Options specific to weka.classifiers.trees.J48:

-U
    Use unpruned tree.
-O
    Do not collapse tree.
-C <pruning confidence>
    Set confidence threshold for pruning.
    (default 0.25)
-M <minimum number of instances>
    Set minimum number of instances per leaf.
    (default 2)
-R
    Use reduced error pruning.
-N <number of folds>
    Set number of folds for reduced error
    pruning. One fold is used as pruning set.
    (default 3)
-B
    Use binary splits only.
-S
    Don‘t perform subtree raising.
-L
    Do not clean up after the tree has been built.
-A
    Laplace smoothing for predicted probabilities.
-J
    Do not use MDL correction for info gain on numeric attributes.
-Q <seed>
    Seed for random data shuffling (default 1).

weka.core

weka核心包，基本所有类都与他有联系

核心包中的关键类：Attribute：包含attribute’s name, its type, and, in the case of a nominal or string attribute, its possible values

Instance：contains the attribute values of a particular instance

Instances：holds an ordered set of instances—in other words, a dataset

weka.classifiers

内容：contains implementations of most of the algorithms for clas-sification  and  numeric  prediction

关键抽象类：Classifier---->>defines the general structure of any  scheme  for  classification  or  numeric  prediction

包含三个核心方法：buildClassifier(), classifyInstance(),distributionForInstance()

继承这个抽象类的例子：

• weka.classifiers.trees.DecisionStump
• 覆写了distributionForInstance()
• 包含getRevision()，simply returns the revision number of the classifier，used  by  Weka  maintainers  when  diagnosing  and debugging  problems  reported  by  users.
• 包含globalInfo()，returns  a  string describing  the  classifier,  which,  along  with  the  scheme’s  options
• 包含toString()， returns a textual representation of the classifier
• 包含toSource()，s used to obtain a source code repre-sentation  of  the  learned  classifier
• 包含main()，called  when  you  ask  for a  decision  stump  from  the  command  line，相当于执行这个类的入口
• 包含getCapabilities() ，called  by  the  generic  object  editor  to  provide information about the capabilities of a learning scheme

其他的一些比较重要的包

weka.associations

：contains association-rule  learners

weka.clusterers 

：contains  methods  for  unsupervised  learning.包含非监督学习方法

weka.datagenerators

：产生人工数据

weka.estimators package

：computes  different  types  of  probability  distribution

 weka.filters

：提供数据清理的相关方法